Artificial Intelligence in Agriculture最新文献

Mapping hazelnut orchards can facilitate land planning and utilization policies, supporting the development of cooperative precision farming systems. The present work faces the detection of hazelnut crops using optical and radar remote sensing data. A comparative study of Machine Learning techniques is presented. The system proposed utilizes multi-temporal data from the Sentinel-1 and Sentinel-2 datasets extracted over several years and processed with cloud tools. We provide a dataset of 62,982 labeled samples, with 16,561 samples belonging to the ‘hazelnut’ class and 46,421 samples belonging to the ‘other’ class, collected in 8 heterogeneous geographical areas of the Viterbo province. Two different comparative tests are conducted: firstly, we use a Nested 5-Fold Cross-Validation methodology to train, optimize, and compare different Machine Learning algorithms on a single area. In a second experiment, the algorithms were trained on one area and tested on the remaining seven geographical areas. The developed study demonstrates how AI analysis applied to Sentinel-1 and Sentinel-2 data is a valid technology for hazelnut mapping. From the results, it emerges that Random Forest is the classifier with the highest generalizability, achieving the best performance in the second test with an accuracy of 96% and an F1 score of 91% for the ‘hazelnut’ class.

Autonomous navigation in farmlands is one of the key technologies for achieving autonomous management in maize fields. Among various navigation techniques, visual navigation using widely available RGB images is a cost-effective solution. However, current mainstream methods for maize crop row detection often rely on highly specialized, manually devised heuristic rules, limiting the scalability of these methods. To simplify the solution and enhance its universality, we propose an innovative crop row annotation strategy. This strategy, by simulating the strip-like structure of the crop row's central area, effectively avoids interference from lateral growth of crop leaves. Based on this, we developed a deep learning network with a dual-branch architecture, InstaCropNet, which achieves end-to-end segmentation of crop row instances. Subsequently, through the row anchor segmentation technique, we accurately locate the positions of different crop row instances and perform line fitting. Experimental results demonstrate that our method has an average angular deviation of no more than 2°, and the accuracy of crop row detection reaches 96.5%.

The issue of food security continues to be a prominent global concern, affecting a significant number of individuals who experience the adverse effects of hunger and malnutrition. The finding of a solution of this intricate issue necessitates the implementation of novel and paradigm-shifting methodologies in agriculture and food sector. In recent times, the domain of artificial intelligence (AI) has emerged as a potent tool capable of instigating a profound influence on the agriculture and food sectors. AI technologies provide significant advantages by optimizing crop cultivation practices, enabling the use of predictive modelling and precision agriculture techniques, and aiding efficient crop monitoring and disease identification. Additionally, AI has the potential to optimize supply chain operations, storage management, transportation systems, and quality assurance processes. It also tackles the problem of food loss and waste through post-harvest loss reduction, predictive analytics, and smart inventory management. This study highlights that how by utilizing the power of AI, we could transform the way we produce, distribute, and manage food, ultimately creating a more secure and sustainable future for all.

Factory recirculating aquaculture system (RAS) is facing in a stage of continuous research and technological innovation. Intelligent aquaculture is an important direction for the future development of aquaculture. However, the RAS nowdays still has poor self-learning and optimal decision-making capabilities, which leads to high aquaculture cost and low running efficiency. In this paper, a precise aeration strategy based on deep learning is designed for improving the healthy growth of breeding objects. Firstly, the situation perception driven by computer vision is used to detect the hypoxia behavior. Then combined with the biological energy model, it is constructed to calculate the breeding objects oxygen consumption. Finally, the optimal adaptive aeration strategy is generated according to hypoxia behavior judgement and biological energy model. Experimental results show that the energy consumption of proposed precise aeration strategy decreased by 26.3% compared with the manual control and 12.8% compared with the threshold control. Meanwhile, stable water quality conditions accelerated breeding objects growth, and the breeding cycle with the average weight of 400 g was shortened from 5 to 6 months to 3–4 months.

This work focuses on a novel lightweight machine learning approach to the task of plant disease classification, posing as a core component of a larger grow-light smart monitoring system. To the extent of our knowledge, this work is the first to implement lightweight convolutional neural network architectures leveraging down-scaled versions of inception blocks, residual connections, and dense residual connections applied without pre-training to the PlantVillage dataset. The novel contributions of this work include the proposal of a smart monitoring framework outline; responsible for detection and classification of ailments via the devised lightweight networks as well as interfacing with LED grow-light fixtures to optimize environmental parameters and lighting control for the growth of plants in a greenhouse system. Lightweight adaptation of dense residual connections achieved the best balance of minimizing model parameters and maximizing performance metrics with accuracy, precision, recall, and F1-scores of 96.75%, 97.62%, 97.59%, and 97.58% respectively, while consisting of only 228,479 model parameters. These results are further compared against various full-scale state-of-the-art model architectures trained on the PlantVillage dataset, of which the proposed down-scaled lightweight models were capable of performing equally to, if not better than many large-scale counterparts with drastically less computational requirements.

The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural scenarios. However, a conspicuous gap endures in the literature concerning the training and testing of models across disparate environmental conditions. Predominant methodologies either delineate a single dataset distribution into training, validation, and testing subsets or merge datasets from diverse conditions or distributions before their division into the subsets. Our study aims to ameliorate this gap by extending to several broadleaf weed categories across varied distributions, evaluating the impact of training convolutional neural networks on datasets specific to particular conditions or distributions, and assessing their performance in entirely distinct settings through three experiments. By evaluating diverse network architectures and training approaches (finetuning versus feature extraction), testing various architectures, employing different training strategies, and amalgamating data, we devised straightforward guidelines to ensure the model's deployability in contrasting environments with sustained precision and accuracy.

In Experiment 1, conducted in a uniform environment, accuracy ranged from 80% to 100% across all models and training strategies, with finetune mode achieving a superior performance of 94% to 99.9% compared to the feature extraction mode at 80% to 92.96%. Experiment 2 underscored a significant performance decline, with accuracy figures between 25% and 60%, primarily at 40%, when the origin of the test data deviated from the train and validation sets. Experiment 3, spotlighting dataset and distribution amalgamation, yielded promising accuracy metrics, notably a peak of 99.6% for ResNet in finetuning mode to a low of 69.9% for InceptionV3 in feature extraction mode. These pivotal findings emphasize that merging data from diverse distributions, coupled with finetuned training on advanced architectures like ResNet and MobileNet, markedly enhances performance, contrasting with the relatively lower performance exhibited by simpler networks like AlexNet. Our results suggest that embracing data diversity and flexible training methodologies are crucial for optimizing weed classification models when disparate data distributions are available. This study gives a practical alternative for treating diverse datasets with real-world agricultural variances.

Leaf blight spot disease, caused by bacteria and fungi, poses a threat to plant health, leading to leaf discoloration and diminished agricultural yield. In response, we present a MobileNetV3 based classifier designed for the Jasmine plant, leveraging lightweight Convolutional Neural Networks (CNNs) to accurately identify disease stages. The model integrates depth wise convolution layers and max pool layers for enhanced feature extraction, focusing on crucial low level features indicative of the disease. Through preprocessing techniques, including data augmentation with Conditional GAN and Particle Swarm Optimization for feature selection, the classifier achieves robust performance. Evaluation on curated datasets demonstrates an outstanding 97% training accuracy, highlighting its efficacy. Real world testing with diverse conditions, such as extreme camera angles and varied lighting, attests to the model's resilience, yielding test accuracies between 94% and 96%. The dataset's tailored design for CNN based classification ensures result reliability. Importantly, the model's lightweight classification, marked by fast computation time and reduced size, positions it as an efficient solution for real time applications. This comprehensive approach underscores the proposed classifier's significance in addressing leaf blight spot disease challenges in commercial crops.

Since determining the weight of pigs during large-scale breeding and production is challenging, using non-contact estimation methods is vital. This study proposed a novel pig weight prediction method based on a modified mask region-convolutional neural network (mask R-CNN). The modified approach used ResNeSt as the backbone feature extraction network to enhance the image feature extraction ability. The feature pyramid network (FPN) was added to the backbone feature extraction network for multi-scale feature fusion. The channel attention mechanism (CAM) and spatial attention mechanism (SAM) were introduced in the region proposal network (RPN) for the adaptive integration of local features and their global dependencies to capture global information, ultimately improving image segmentation accuracy. The modified network obtained a precision rate (P), recall rate (R), and mean average precision (MAP) of 90.33%, 89.85%, and 95.21%, respectively, effectively segmenting the pig regions in the images. Five image features, namely the back area (A), body length (L), body width (W), average depth (AD), and eccentricity (E), were investigated. The pig depth images were used to build five regression algorithms (ordinary least squares (OLS), AdaBoost, CatBoost, XGBoost, and random forest (RF)) for weight value prediction. AdaBoost achieved the best prediction result with a coefficient of determination (R²) of 0.987, a mean absolute error (MAE) of 2.96 kg, a mean square error (MSE) of 12.87 kg², and a mean absolute percentage error (MAPE) of 8.45%. The results demonstrated that the machine learning models effectively predicted the weight values of the pigs, providing technical support for intelligent pig farm management.

For the purpose of monitoring apple fruits effectively throughout the entire growth period in smart orchards. A lightweight model named YOLOv8n-ShuffleNetv2-Ghost-SE was proposed. The ShuffleNetv2 basic modules and down-sampling modules were alternately connected, replacing the Backbone of YOLOv8n model. The Ghost modules replaced the Conv modules and the C2fGhost modules replaced the C2f modules in the Neck part of the YOLOv8n. ShuffleNetv2 reduced the memory access cost through channel splitting operations. The Ghost module combined linear and non-linear convolutions to reduce the network computation cost. The Wise-IoU (WIoU) replaced the CIoU for calculating the bounding box regression loss, which dynamically adjusted the anchor box quality threshold and gradient gain allocation strategy, optimizing the size and position of predicted bounding boxes. The Squeeze-and-Excitation (SE) was embedded in the Backbone and Neck part of YOLOv8n to enhance the representation ability of feature maps. The algorithm ensured high precision while having small model size and fast detection speed, which facilitated model migration and deployment. Using 9652 images validated the effectiveness of the model. The YOLOv8n-ShuffleNetv2-Ghost-SE model achieved Precision of 94.1%, Recall of 82.6%, mean Average Precision of 91.4%, model size of 2.6 MB, parameters of 1.18 M, FLOPs of 3.9 G, and detection speed of 39.37 fps. The detection speeds on the Jetson Xavier NX development board were 3.17 fps. Comparisons with advanced models including Faster R-CNN, SSD, YOLOv5s, YOLOv7‑tiny, YOLOv8s, YOLOv8n, MobileNetv3_small-Faster, MobileNetv3_small-Ghost, ShuflleNetv2-Faster, ShuflleNetv2-Ghost, ShuflleNetv2-Ghost-CBAM, ShuflleNetv2-Ghost-ECA, and ShuflleNetv2-Ghost-CA demonstrated that the method achieved smaller model and faster detection speed. The research can provide reference for the development of smart devices in apple orchards.

The food industry typically relies heavily on manual operations with high proficiency and skills. According to the quality inspection process, a baby corn with black marks or blemishes is considered a defect or unqualified class which should be discarded. Quality inspection and sorting of agricultural products like baby corn are labor-intensive and time-consuming. The main goal of this work is to develop an automated quality inspection framework to differentiate between ‘pass’ and ‘fail’ categories based on baby corn images. A traditional image processing method using a threshold principle is compared with relatively more advanced deep learning models. Particularly, Convolutional neural networks, specific sub-types of deep learning models, were implemented. Thorough experiments on choices of network architectures and their hyperparameters were conducted and compared. A Shapley additive explanations (SHAP) framework was further utilized for network interpretation purposes. The EfficientNetB5 networks with relatively larger input sizes yielded up to 99.06% accuracy as the best performance against 95.28% obtained from traditional image processing. Incorporating a region of interest identification, several model experiments, data application on baby corn images, and the SHAP framework are our main contributions. Our proposed quality inspection system to automatically differentiate baby corn images provides a potential pipeline to further support the agricultural production process.